Can not know how to use minibatchqueue for a deep learning network that takes input as 4-D numbers and output 3 numbers through fully-connected layer.
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There is a MATLAB example that uses minibatchqueue for input date as 4-D (image) and output as categorical. What I need is to update this example to accept output to be three numberical values (through a 3-fully connected layer).
The MATLAB example is:
[XTrain,YTrain] = digitTrain4DArrayData;
dsX = arrayDatastore(XTrain,IterationDimension=4);
dsY = arrayDatastore(YTrain);
dsTrain = combine(dsX,dsY);
classes = categories(YTrain);
numClasses = numel(classes);
net = dlnetwork;
layers = [
imageInputLayer([28 28 1],Mean=mean(XTrain,4))
convolution2dLayer(5,20)
reluLayer
convolution2dLayer(3,20,Padding=1)
reluLayer
convolution2dLayer(3,20,Padding=1)
reluLayer
fullyConnectedLayer(numClasses)
softmaxLayer];
net = addLayers(net,layers);
net = initialize(net);
miniBatchSize = 128;
mbq = minibatchqueue(dsTrain,...
MiniBatchSize=miniBatchSize,...
PartialMiniBatch="discard",...
MiniBatchFcn=@preprocessMiniBatch,...
MiniBatchFormat=["SSCB",""]);
function [X,Y] = preprocessMiniBatch(XCell,YCell)
% Extract image data from the cell array and concatenate over fourth
% dimension to add a third singleton dimension, as the channel
% dimension.
X = cat(4,XCell{:});
% Extract label data from cell and concatenate.
Y = cat(2,YCell{:});
% One-hot encode labels.
Y = onehotencode(Y,1);
end
Again, what I need is to know how to modify the code to accept three regression values at fully connected output layer.
Actually, I tried alot and alot without success. I think the main trick is the update that should be done inside this function: preprocessMiniBatch (defined above).
Thanks
3 Comments
Umar
on 21 Jul 2024 at 4:06
Hi Nader,
In case, you could not figure out, I am providing code snippets for network architecture modification and update preprocessing function
Network Architecture Modification
layers = [
imageInputLayer([28 28 1], 'Mean', mean(XTrain, 4))
convolution2dLayer(5, 20)
reluLayer
convolution2dLayer(3, 20, 'Padding', 1)
reluLayer
convolution2dLayer(3, 20, 'Padding', 1)
reluLayer
fullyConnectedLayer(3) % Three output nodes for regression
regressionLayer]; % Use regressionLayer for regression tasks
Update Preprocessing Function
function [X, Y] = preprocessMiniBatch(XCell, YCell)
% Extract image data from the cell array and concatenate over the fourth dimension
X = cat(4, XCell{:});
% Extract and concatenate regression labels
Y = cat(2, YCell{:});
end
Goodluck!
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